{"id":"W4392791522","doi":"10.2196/55199","title":"Mining User Reviews From Hypertension Management Mobile Health Apps to Explore Factors Influencing User Satisfaction and Their Asymmetry: Comparative Study","year":2024,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latent Dirichlet allocation; mHealth; Preprint; User satisfaction; Computer science; Medicine; Applied psychology; Psychology; Topic model; World Wide Web; Psychological intervention; Nursing; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003714521,0.0003838593,0.000744612,0.003373943,0.0006583921,0.001338886,0.0004525917,0.0004563378,0.001612502],"category_scores_gemma":[0.03051196,0.0002271694,0.0009781842,0.003295052,0.0003200977,0.001582487,0.0008037729,0.0004813702,0.0005658387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006143262,"about_ca_system_score_gemma":0.0008593119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005136939,"about_ca_topic_score_gemma":0.007580996,"domain_scores_codex":[0.9964839,0.001374743,0.0005437392,0.0004917403,0.0008899741,0.0002158998],"domain_scores_gemma":[0.9674823,0.01841419,0.005249686,0.001602058,0.006514792,0.0007368577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008806738,0.0006992553,0.9073683,0.001221429,0.0004522688,0.0003393602,0.008758302,0.0002669361,0.0008930132,0.0002420211,0.00247816,0.07640029],"study_design_scores_gemma":[0.00004682311,0.0005601567,0.9804589,0.0002301843,0.0003750555,0.0004659975,0.008008024,0.005716304,0.0007420488,0.0001461312,0.003201205,0.00004921994],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964507,0.0004640105,0.0007686649,0.00006756868,0.00001194155,0.0001752404,0.001269157,0.00002395178,0.0007687492],"genre_scores_gemma":[0.9947136,0.0003373476,0.001731394,0.00006851668,0.00003085331,0.0002813055,0.002348521,0.00001261306,0.0004758532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005136939,"threshold_uncertainty_score":0.0196445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.212337081414552,"score_gpt":0.4794478673245686,"score_spread":0.2671107859100165,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}